
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	Dict       			=  {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""mobilenet_v1"""
  def __init__(					self     :					int     ,				UpperCamelCase__     :					Tuple=3     ,				UpperCamelCase__     :					Optional[int]=224     ,				UpperCamelCase__     :					List[Any]=1.0     ,				UpperCamelCase__     :					str=8     ,				UpperCamelCase__     :					Optional[Any]="relu6"     ,				UpperCamelCase__     :					int=True     ,				UpperCamelCase__     :					Tuple=0.999     ,				UpperCamelCase__     :					Optional[Any]=0.02     ,				UpperCamelCase__     :					Dict=0.001     ,				**UpperCamelCase__     :					int     ,				)     ->      Optional[int]:







    """simple docstring"""






    super().__init__(**UpperCamelCase__       )

    if depth_multiplier <= 0:
      raise ValueError("""depth_multiplier must be greater than zero."""       )

    __magic_name__								=			num_channels
    __magic_name__								=			image_size
    __magic_name__								=			depth_multiplier
    __magic_name__								=			min_depth
    __magic_name__								=			hidden_act
    __magic_name__								=			tf_padding
    __magic_name__								=			classifier_dropout_prob
    __magic_name__								=			initializer_range
    __magic_name__								=			layer_norm_eps







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						version.parse("""1.11"""	)
  @property
  def 				_lowercase				(					self     :					Dict       )     ->      Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    return OrderedDict([("""pixel_values""", {0: """batch"""})]       )
  @property
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    if self.task == "image-classification":
      return OrderedDict([("""logits""", {0: """batch"""})]       )
    else:
      return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]       )





  @property
  def 				_lowercase				(					self     :					str       )     ->      float:







    """simple docstring"""






    return 1E-4



def a__       (			A_=28123   ):






  '''simple docstring'''





  __magic_name__								=			[1] * (limit + 1)

  for i in range(2,	int(limit**0.5   ) + 1   ):
    sum_divs[i * i] += i
    for k in range(i + 1,	limit // i + 1   ):
      sum_divs[k * i] += k + i

  __magic_name__								=			set()
  __magic_name__								=			0

  for n in range(1,	limit + 1   ):
    if sum_divs[n] > n:
      abundants.add(A_   )

    if not any((n - a in abundants) for a in abundants   ):
      res += n

  return res


if __name__ == "__main__":
    print(solution())

